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Reproducible Example

In [1]: import pandas as pd

In [2]: pd.__version__
Out[2]: '2.1.0.dev0+5.g8d2a4e11d1'

In [3]: import numpy as np

In [4]: np.__version__
Out[4]: '1.23.5'

In [5]: s = pd.Series([1,2,3])

In [6]: s.astype(np.integer)
Out[6]:
0    1
1    2
2    3
dtype: int32

In [7]: np.dtype(np.integer)
<ipython-input-7-c11ccc0059df>:1: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.
  np.dtype(np.integer)
Out[7]: dtype('int32')

In [8]: from pandas.core.dtypes.common import pandas_dtype

In [9]: pandas_dtype(np.integer)
Out[9]: dtype('int32')

Issue Description

For some reason, if you call Series.astype(np.integer), the warning from numpy about calling np.dtype(np.integer) is suppressed.

This came up in some pandas-stubs testing. pandas.core.dtypes.common.pandas_dtype() calles np.dtype(), but we don't print the deprecation warning.

The way we saw it is that it got printed by pytest

Expected Behavior

We should print the numpy deprecation warning.

Installed Versions

INSTALLED VERSIONS ------------------ commit : 8d2a4e11d136af439de92ef1a970a1ae0edde4dc python : 3.8.16.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19045 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 2.1.0.dev0+5.g8d2a4e11d1 numpy : 1.23.5 pytz : 2022.7.1 dateutil : 2.8.2 setuptools : 66.1.1 pip : 23.0 Cython : 0.29.32 pytest : 7.2.1 hypothesis : 6.68.1 sphinx : 4.5.0 blosc : None feather : None xlsxwriter : 3.0.8 lxml.etree : 4.9.2 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : 2.9.3 jinja2 : 3.1.2 IPython : 8.10.0 pandas_datareader: None bs4 : 4.11.2 bottleneck : 1.3.6 brotli : fastparquet : 2023.2.0 fsspec : 2023.1.0 gcsfs : 2023.1.0 matplotlib : 3.6.3 numba : 0.56.4 numexpr : 2.8.3 odfpy : None openpyxl : 3.1.0 pandas_gbq : None pyarrow : 10.0.1 pyreadstat : 1.2.0 pyxlsb : 1.0.10 s3fs : 2023.1.0 scipy : 1.10.0 snappy : sqlalchemy : 2.0.3 tables : 3.7.0 tabulate : 0.9.0 xarray : 2023.1.0 xlrd : 2.0.1 zstandard : 0.19.0 tzdata : None qtpy : None pyqt5 : None

Comment From: dannyi96

take

Comment From: luke396

Furthermore, many warning.filterwarnings have been added to suppress DeprecationWarning from numpy. Would it be a better option to simply ignore this warning?